One of the properties used to class fiber such as cotton is the color of the fiber. Traditionally, the classification of fiber relies predominantly upon the senses and judgement of human classers who visually inspect a fiber sample and assign it a grade accordingly.
There are incentives to replace human classers with high-volume instrumentation. For example, instrumentation generally tends to be faster, more reliable, more repeatable, and less expensive than the manual labor which it replaces. However, in side-by-side tests, prior-art classification instrumentation tends to assign a higher grade to the fiber samples tested than do human classers. Therefore, such instrumentation has not replaced human classers in final or more critical classification steps.
What is needed, therefore, is a system for detecting visual properties of fiber samples and assigning grades to the fiber samples, where the correlation between the grades assigned by the system have a better correlation to the grades assigned by human classers than do the present fiber classification devices.